Overview

Dataset statistics

Number of variables19
Number of observations2186
Missing cells6958
Missing cells (%)16.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.3 MiB
Average record size in memory642.0 B

Variable types

Numeric12
Categorical7

Alerts

ReporterISO has constant value "CHN"Constant
FlowDesc has constant value "Import"Constant
PartnerISO has a high cardinality: 222 distinct valuesHigh cardinality
Key has a high cardinality: 2186 distinct valuesHigh cardinality
Country Code has a high cardinality: 205 distinct valuesHigh cardinality
gdp has a high cardinality: 1985 distinct valuesHigh cardinality
j has a high cardinality: 192 distinct valuesHigh cardinality
RefYear is highly overall correlated with Period and 6 other fieldsHigh correlation
Period is highly overall correlated with RefYear and 6 other fieldsHigh correlation
Cifvalue is highly overall correlated with PrimaryValueHigh correlation
PrimaryValue is highly overall correlated with CifvalueHigh correlation
year is highly overall correlated with RefYear and 6 other fieldsHigh correlation
sum_pos_tweets is highly overall correlated with RefYear and 6 other fieldsHigh correlation
count_tweets is highly overall correlated with RefYear and 6 other fieldsHigh correlation
sum_political_tweets is highly overall correlated with RefYear and 6 other fieldsHigh correlation
sum_likes is highly overall correlated with RefYear and 6 other fieldsHigh correlation
sum_retweeets is highly overall correlated with RefYear and 6 other fieldsHigh correlation
Country Code has 138 (6.3%) missing valuesMissing
year has 138 (6.3%) missing valuesMissing
gdp has 138 (6.3%) missing valuesMissing
population has 138 (6.3%) missing valuesMissing
j has 268 (12.3%) missing valuesMissing
dist has 298 (13.6%) missing valuesMissing
sum_pos_tweets has 1168 (53.4%) missing valuesMissing
count_tweets has 1168 (53.4%) missing valuesMissing
sum_political_tweets has 1168 (53.4%) missing valuesMissing
sum_likes has 1168 (53.4%) missing valuesMissing
sum_retweeets has 1168 (53.4%) missing valuesMissing
PartnerISO is uniformly distributedUniform
Key is uniformly distributedUniform
Country Code is uniformly distributedUniform
j is uniformly distributedUniform
Key has unique valuesUnique
sum_pos_tweets has 71 (3.2%) zerosZeros
sum_likes has 109 (5.0%) zerosZeros
sum_retweeets has 109 (5.0%) zerosZeros

Reproduction

Analysis started2023-04-10 20:14:04.031203
Analysis finished2023-04-10 20:16:55.203460
Duration2 minutes and 51.17 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

RefYear
Real number (ℝ)

Distinct10
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2016.511
Minimum2012
Maximum2021
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.2 KiB
2023-04-10T22:16:55.257922image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum2012
5-th percentile2012
Q12014
median2017
Q32019
95-th percentile2021
Maximum2021
Range9
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.8745101
Coefficient of variation (CV)0.001425487
Kurtosis-1.2252865
Mean2016.511
Median Absolute Deviation (MAD)2.5
Skewness-0.0067243127
Sum4408093
Variance8.2628085
MonotonicityIncreasing
2023-04-10T22:16:55.348626image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
2018 220
10.1%
2019 220
10.1%
2021 220
10.1%
2013 219
10.0%
2016 219
10.0%
2017 219
10.0%
2020 219
10.0%
2012 218
10.0%
2014 217
9.9%
2015 215
9.8%
ValueCountFrequency (%)
2012 218
10.0%
2013 219
10.0%
2014 217
9.9%
2015 215
9.8%
2016 219
10.0%
2017 219
10.0%
2018 220
10.1%
2019 220
10.1%
2020 219
10.0%
2021 220
10.1%
ValueCountFrequency (%)
2021 220
10.1%
2020 219
10.0%
2019 220
10.1%
2018 220
10.1%
2017 219
10.0%
2016 219
10.0%
2015 215
9.8%
2014 217
9.9%
2013 219
10.0%
2012 218
10.0%

Period
Real number (ℝ)

Distinct10
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2016.511
Minimum2012
Maximum2021
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.2 KiB
2023-04-10T22:16:55.446344image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum2012
5-th percentile2012
Q12014
median2017
Q32019
95-th percentile2021
Maximum2021
Range9
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.8745101
Coefficient of variation (CV)0.001425487
Kurtosis-1.2252865
Mean2016.511
Median Absolute Deviation (MAD)2.5
Skewness-0.0067243127
Sum4408093
Variance8.2628085
MonotonicityIncreasing
2023-04-10T22:16:55.538549image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
2018 220
10.1%
2019 220
10.1%
2021 220
10.1%
2013 219
10.0%
2016 219
10.0%
2017 219
10.0%
2020 219
10.0%
2012 218
10.0%
2014 217
9.9%
2015 215
9.8%
ValueCountFrequency (%)
2012 218
10.0%
2013 219
10.0%
2014 217
9.9%
2015 215
9.8%
2016 219
10.0%
2017 219
10.0%
2018 220
10.1%
2019 220
10.1%
2020 219
10.0%
2021 220
10.1%
ValueCountFrequency (%)
2021 220
10.1%
2020 219
10.0%
2019 220
10.1%
2018 220
10.1%
2017 219
10.0%
2016 219
10.0%
2015 215
9.8%
2014 217
9.9%
2013 219
10.0%
2012 218
10.0%

ReporterISO
Categorical

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size145.2 KiB
CHN
2186 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters6558
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCHN
2nd rowCHN
3rd rowCHN
4th rowCHN
5th rowCHN

Common Values

ValueCountFrequency (%)
CHN 2186
100.0%

Length

2023-04-10T22:16:55.636123image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-10T22:16:55.743577image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
chn 2186
100.0%

Most occurring characters

ValueCountFrequency (%)
C 2186
33.3%
H 2186
33.3%
N 2186
33.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 6558
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
C 2186
33.3%
H 2186
33.3%
N 2186
33.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 6558
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
C 2186
33.3%
H 2186
33.3%
N 2186
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6558
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
C 2186
33.3%
H 2186
33.3%
N 2186
33.3%

FlowDesc
Categorical

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size151.6 KiB
Import
2186 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters13116
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowImport
2nd rowImport
3rd rowImport
4th rowImport
5th rowImport

Common Values

ValueCountFrequency (%)
Import 2186
100.0%

Length

2023-04-10T22:16:55.829965image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-10T22:16:55.937332image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
import 2186
100.0%

Most occurring characters

ValueCountFrequency (%)
I 2186
16.7%
m 2186
16.7%
p 2186
16.7%
o 2186
16.7%
r 2186
16.7%
t 2186
16.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 10930
83.3%
Uppercase Letter 2186
 
16.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
m 2186
20.0%
p 2186
20.0%
o 2186
20.0%
r 2186
20.0%
t 2186
20.0%
Uppercase Letter
ValueCountFrequency (%)
I 2186
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 13116
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
I 2186
16.7%
m 2186
16.7%
p 2186
16.7%
o 2186
16.7%
r 2186
16.7%
t 2186
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13116
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
I 2186
16.7%
m 2186
16.7%
p 2186
16.7%
o 2186
16.7%
r 2186
16.7%
t 2186
16.7%

PartnerISO
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct222
Distinct (%)10.2%
Missing0
Missing (%)0.0%
Memory size145.5 KiB
W00
 
10
CUW
 
10
BES
 
10
NCL
 
10
VUT
 
10
Other values (217)
2136 

Length

Max length7
Median length3
Mean length3.0182983
Min length3

Characters and Unicode

Total characters6598
Distinct characters35
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowW00
2nd rowAFG
3rd rowALB
4th rowDZA
5th rowAND

Common Values

ValueCountFrequency (%)
W00 10
 
0.5%
CUW 10
 
0.5%
BES 10
 
0.5%
NCL 10
 
0.5%
VUT 10
 
0.5%
NZL 10
 
0.5%
NIC 10
 
0.5%
NER 10
 
0.5%
NGA 10
 
0.5%
19,00 F 10
 
0.5%
Other values (212) 2086
95.4%

Length

2023-04-10T22:16:56.033128image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
w00 10
 
0.5%
bdi 10
 
0.5%
dom 10
 
0.5%
brn 10
 
0.5%
alb 10
 
0.5%
dza 10
 
0.5%
and 10
 
0.5%
ago 10
 
0.5%
atg 10
 
0.5%
aze 10
 
0.5%
Other values (213) 2096
95.4%

Most occurring characters

ValueCountFrequency (%)
A 488
 
7.4%
R 480
 
7.3%
N 426
 
6.5%
M 418
 
6.3%
S 395
 
6.0%
B 359
 
5.4%
L 348
 
5.3%
T 320
 
4.8%
G 320
 
4.8%
C 289
 
4.4%
Other values (25) 2755
41.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 6422
97.3%
Decimal Number 136
 
2.1%
Space Separator 20
 
0.3%
Other Punctuation 10
 
0.2%
Connector Punctuation 10
 
0.2%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 488
 
7.6%
R 480
 
7.5%
N 426
 
6.6%
M 418
 
6.5%
S 395
 
6.2%
B 359
 
5.6%
L 348
 
5.4%
T 320
 
5.0%
G 320
 
5.0%
C 289
 
4.5%
Other values (16) 2579
40.2%
Decimal Number
ValueCountFrequency (%)
9 48
35.3%
0 40
29.4%
1 29
21.3%
7 10
 
7.4%
5 9
 
6.6%
Space Separator
ValueCountFrequency (%)
  10
50.0%
10
50.0%
Other Punctuation
ValueCountFrequency (%)
, 10
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 6422
97.3%
Common 176
 
2.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 488
 
7.6%
R 480
 
7.5%
N 426
 
6.6%
M 418
 
6.5%
S 395
 
6.2%
B 359
 
5.6%
L 348
 
5.4%
T 320
 
5.0%
G 320
 
5.0%
C 289
 
4.5%
Other values (16) 2579
40.2%
Common
ValueCountFrequency (%)
9 48
27.3%
0 40
22.7%
1 29
16.5%
  10
 
5.7%
, 10
 
5.7%
7 10
 
5.7%
_ 10
 
5.7%
10
 
5.7%
5 9
 
5.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6588
99.8%
None 10
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 488
 
7.4%
R 480
 
7.3%
N 426
 
6.5%
M 418
 
6.3%
S 395
 
6.0%
B 359
 
5.4%
L 348
 
5.3%
T 320
 
4.9%
G 320
 
4.9%
C 289
 
4.4%
Other values (24) 2745
41.7%
None
ValueCountFrequency (%)
  10
100.0%

Cifvalue
Real number (ℝ)

Distinct2184
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.8120334 × 1010
Minimum9
Maximum2.6843627 × 1012
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.2 KiB
2023-04-10T22:16:56.166093image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile8953.5
Q118730681
median2.8651095 × 108
Q33.8242945 × 109
95-th percentile5.302828 × 1010
Maximum2.6843627 × 1012
Range2.6843627 × 1012
Interquartile range (IQR)3.8055638 × 109

Descriptive statistics

Standard deviation1.3730537 × 1011
Coefficient of variation (CV)7.5774194
Kurtosis216.21251
Mean1.8120334 × 1010
Median Absolute Deviation (MAD)2.8648118 × 108
Skewness14.312647
Sum3.9611051 × 1013
Variance1.8852766 × 1022
MonotonicityNot monotonic
2023-04-10T22:16:56.317690image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2753 2
 
0.1%
71 2
 
0.1%
1.818199228 × 10121
 
< 0.1%
3421521148 1
 
< 0.1%
21774777 1
 
< 0.1%
33160256 1
 
< 0.1%
40063 1
 
< 0.1%
2172086000 1
 
< 0.1%
81763041 1
 
< 0.1%
2825845569 1
 
< 0.1%
Other values (2174) 2174
99.5%
ValueCountFrequency (%)
9 1
< 0.1%
13 1
< 0.1%
31 1
< 0.1%
33 1
< 0.1%
34 1
< 0.1%
39 1
< 0.1%
45 1
< 0.1%
71 2
0.1%
72 1
< 0.1%
76 1
< 0.1%
ValueCountFrequency (%)
2.684362679 × 10121
< 0.1%
2.133605397 × 10121
< 0.1%
2.079285499 × 10121
< 0.1%
2.069567865 × 10121
< 0.1%
1.959234625 × 10121
< 0.1%
1.949992315 × 10121
< 0.1%
1.843792939 × 10121
< 0.1%
1.818199228 × 10121
< 0.1%
1.679564325 × 10121
< 0.1%
1.587920688 × 10121
< 0.1%

PrimaryValue
Real number (ℝ)

Distinct2184
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.8120334 × 1010
Minimum9
Maximum2.6843627 × 1012
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.2 KiB
2023-04-10T22:16:56.456025image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile8953.5
Q118730681
median2.8651095 × 108
Q33.8242945 × 109
95-th percentile5.302828 × 1010
Maximum2.6843627 × 1012
Range2.6843627 × 1012
Interquartile range (IQR)3.8055638 × 109

Descriptive statistics

Standard deviation1.3730537 × 1011
Coefficient of variation (CV)7.5774194
Kurtosis216.21251
Mean1.8120334 × 1010
Median Absolute Deviation (MAD)2.8648118 × 108
Skewness14.312647
Sum3.9611051 × 1013
Variance1.8852766 × 1022
MonotonicityNot monotonic
2023-04-10T22:16:56.601641image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2753 2
 
0.1%
71 2
 
0.1%
1.818199228 × 10121
 
< 0.1%
3421521148 1
 
< 0.1%
21774777 1
 
< 0.1%
33160256 1
 
< 0.1%
40063 1
 
< 0.1%
2172086000 1
 
< 0.1%
81763041 1
 
< 0.1%
2825845569 1
 
< 0.1%
Other values (2174) 2174
99.5%
ValueCountFrequency (%)
9 1
< 0.1%
13 1
< 0.1%
31 1
< 0.1%
33 1
< 0.1%
34 1
< 0.1%
39 1
< 0.1%
45 1
< 0.1%
71 2
0.1%
72 1
< 0.1%
76 1
< 0.1%
ValueCountFrequency (%)
2.684362679 × 10121
< 0.1%
2.133605397 × 10121
< 0.1%
2.079285499 × 10121
< 0.1%
2.069567865 × 10121
< 0.1%
1.959234625 × 10121
< 0.1%
1.949992315 × 10121
< 0.1%
1.843792939 × 10121
< 0.1%
1.818199228 × 10121
< 0.1%
1.679564325 × 10121
< 0.1%
1.587920688 × 10121
< 0.1%

Key
Categorical

HIGH CARDINALITY  UNIFORM  UNIQUE 

Distinct2186
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
W00_2012
 
1
PNG_2018
 
1
NOR_2018
 
1
FSM_2018
 
1
MHL_2018
 
1
Other values (2181)
2181 

Length

Max length12
Median length8
Mean length8.0182983
Min length8

Characters and Unicode

Total characters17528
Distinct characters40
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2186 ?
Unique (%)100.0%

Sample

1st rowW00_2012
2nd rowAFG_2012
3rd rowALB_2012
4th rowDZA_2012
5th rowAND_2012

Common Values

ValueCountFrequency (%)
W00_2012 1
 
< 0.1%
PNG_2018 1
 
< 0.1%
NOR_2018 1
 
< 0.1%
FSM_2018 1
 
< 0.1%
MHL_2018 1
 
< 0.1%
PLW_2018 1
 
< 0.1%
PAK_2018 1
 
< 0.1%
PAN_2018 1
 
< 0.1%
PRY_2018 1
 
< 0.1%
TGO_2018 1
 
< 0.1%
Other values (2176) 2176
99.5%

Length

2023-04-10T22:16:56.730779image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
19,00 10
 
0.5%
x 10
 
0.5%
w00_2012 1
 
< 0.1%
bih_2012 1
 
< 0.1%
bel_2012 1
 
< 0.1%
cpv_2012 1
 
< 0.1%
brb_2012 1
 
< 0.1%
arm_2012 1
 
< 0.1%
bgd_2012 1
 
< 0.1%
bhr_2012 1
 
< 0.1%
Other values (2178) 2178
98.7%

Most occurring characters

ValueCountFrequency (%)
2 2843
16.2%
0 2445
13.9%
_ 2196
12.5%
1 1996
 
11.4%
A 488
 
2.8%
R 480
 
2.7%
N 426
 
2.4%
M 418
 
2.4%
S 395
 
2.3%
B 359
 
2.0%
Other values (30) 5482
31.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8880
50.7%
Uppercase Letter 6422
36.6%
Connector Punctuation 2196
 
12.5%
Space Separator 20
 
0.1%
Other Punctuation 10
 
0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 488
 
7.6%
R 480
 
7.5%
N 426
 
6.6%
M 418
 
6.5%
S 395
 
6.2%
B 359
 
5.6%
L 348
 
5.4%
G 320
 
5.0%
T 320
 
5.0%
C 289
 
4.5%
Other values (16) 2579
40.2%
Decimal Number
ValueCountFrequency (%)
2 2843
32.0%
0 2445
27.5%
1 1996
22.5%
9 268
 
3.0%
7 229
 
2.6%
5 224
 
2.5%
8 220
 
2.5%
3 219
 
2.5%
6 219
 
2.5%
4 217
 
2.4%
Space Separator
ValueCountFrequency (%)
  10
50.0%
10
50.0%
Connector Punctuation
ValueCountFrequency (%)
_ 2196
100.0%
Other Punctuation
ValueCountFrequency (%)
, 10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 11106
63.4%
Latin 6422
36.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 488
 
7.6%
R 480
 
7.5%
N 426
 
6.6%
M 418
 
6.5%
S 395
 
6.2%
B 359
 
5.6%
L 348
 
5.4%
G 320
 
5.0%
T 320
 
5.0%
C 289
 
4.5%
Other values (16) 2579
40.2%
Common
ValueCountFrequency (%)
2 2843
25.6%
0 2445
22.0%
_ 2196
19.8%
1 1996
18.0%
9 268
 
2.4%
7 229
 
2.1%
5 224
 
2.0%
8 220
 
2.0%
3 219
 
2.0%
6 219
 
2.0%
Other values (4) 247
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 17518
99.9%
None 10
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 2843
16.2%
0 2445
14.0%
_ 2196
12.5%
1 1996
 
11.4%
A 488
 
2.8%
R 480
 
2.7%
N 426
 
2.4%
M 418
 
2.4%
S 395
 
2.3%
B 359
 
2.0%
Other values (29) 5472
31.2%
None
ValueCountFrequency (%)
  10
100.0%

Country Code
Categorical

HIGH CARDINALITY  MISSING  UNIFORM 

Distinct205
Distinct (%)10.0%
Missing138
Missing (%)6.3%
Memory size141.4 KiB
PAN
 
10
VUT
 
10
NZL
 
10
NIC
 
10
NER
 
10
Other values (200)
1998 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters6144
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAFG
2nd rowALB
3rd rowDZA
4th rowAND
5th rowAGO

Common Values

ValueCountFrequency (%)
PAN 10
 
0.5%
VUT 10
 
0.5%
NZL 10
 
0.5%
NIC 10
 
0.5%
NER 10
 
0.5%
NGA 10
 
0.5%
NOR 10
 
0.5%
FSM 10
 
0.5%
MHL 10
 
0.5%
PLW 10
 
0.5%
Other values (195) 1948
89.1%
(Missing) 138
 
6.3%

Length

2023-04-10T22:16:56.841975image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
pan 10
 
0.5%
bgd 10
 
0.5%
alb 10
 
0.5%
dza 10
 
0.5%
and 10
 
0.5%
ago 10
 
0.5%
atg 10
 
0.5%
aze 10
 
0.5%
arg 10
 
0.5%
aus 10
 
0.5%
Other values (195) 1948
95.1%

Most occurring characters

ValueCountFrequency (%)
R 470
 
7.6%
A 460
 
7.5%
N 420
 
6.8%
M 399
 
6.5%
S 350
 
5.7%
B 349
 
5.7%
L 340
 
5.5%
G 320
 
5.2%
T 309
 
5.0%
C 279
 
4.5%
Other values (16) 2448
39.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 6144
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
R 470
 
7.6%
A 460
 
7.5%
N 420
 
6.8%
M 399
 
6.5%
S 350
 
5.7%
B 349
 
5.7%
L 340
 
5.5%
G 320
 
5.2%
T 309
 
5.0%
C 279
 
4.5%
Other values (16) 2448
39.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 6144
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
R 470
 
7.6%
A 460
 
7.5%
N 420
 
6.8%
M 399
 
6.5%
S 350
 
5.7%
B 349
 
5.7%
L 340
 
5.5%
G 320
 
5.2%
T 309
 
5.0%
C 279
 
4.5%
Other values (16) 2448
39.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6144
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
R 470
 
7.6%
A 460
 
7.5%
N 420
 
6.8%
M 399
 
6.5%
S 350
 
5.7%
B 349
 
5.7%
L 340
 
5.5%
G 320
 
5.2%
T 309
 
5.0%
C 279
 
4.5%
Other values (16) 2448
39.8%

year
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct10
Distinct (%)0.5%
Missing138
Missing (%)6.3%
Infinite0
Infinite (%)0.0%
Mean2016.5039
Minimum2012
Maximum2021
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.2 KiB
2023-04-10T22:16:56.937156image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum2012
5-th percentile2012
Q12014
median2017
Q32019
95-th percentile2021
Maximum2021
Range9
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.8716195
Coefficient of variation (CV)0.0014240585
Kurtosis-1.2232164
Mean2016.5039
Median Absolute Deviation (MAD)2
Skewness-0.0013177405
Sum4129800
Variance8.2461987
MonotonicityIncreasing
2023-04-10T22:16:57.028282image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
2014 205
9.4%
2015 205
9.4%
2016 205
9.4%
2017 205
9.4%
2018 205
9.4%
2019 205
9.4%
2020 205
9.4%
2021 205
9.4%
2012 204
9.3%
2013 204
9.3%
(Missing) 138
6.3%
ValueCountFrequency (%)
2012 204
9.3%
2013 204
9.3%
2014 205
9.4%
2015 205
9.4%
2016 205
9.4%
2017 205
9.4%
2018 205
9.4%
2019 205
9.4%
2020 205
9.4%
2021 205
9.4%
ValueCountFrequency (%)
2021 205
9.4%
2020 205
9.4%
2019 205
9.4%
2018 205
9.4%
2017 205
9.4%
2016 205
9.4%
2015 205
9.4%
2014 205
9.4%
2013 204
9.3%
2012 204
9.3%

gdp
Categorical

HIGH CARDINALITY  MISSING 

Distinct1985
Distinct (%)96.9%
Missing138
Missing (%)6.3%
Memory size165.5 KiB
..
 
64
284900000
 
1
401932300
 
1
436999692591.454
 
1
421739210176.152
 
1
Other values (1980)
1980 

Length

Max length16
Median length16
Mean length15.041504
Min length2

Characters and Unicode

Total characters30805
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1984 ?
Unique (%)96.9%

Sample

1st row20203572959.5023
2nd row12319830437.3467
3rd row209058991952.125
4th row3188808942.56713
5th row124998210652.243

Common Values

ValueCountFrequency (%)
.. 64
 
2.9%
284900000 1
 
< 0.1%
401932300 1
 
< 0.1%
436999692591.454 1
 
< 0.1%
421739210176.152 1
 
< 0.1%
12808660528.0617 1
 
< 0.1%
13025239912.2751 1
 
< 0.1%
211953111035.513 1
 
< 0.1%
914736985.430944 1
 
< 0.1%
9846922416.14776 1
 
< 0.1%
Other values (1975) 1975
90.3%
(Missing) 138
 
6.3%

Length

2023-04-10T22:16:57.142989image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
64
 
3.1%
4610096000 1
 
< 0.1%
12319830437.3467 1
 
< 0.1%
209058991952.125 1
 
< 0.1%
3188808942.56713 1
 
< 0.1%
124998210652.243 1
 
< 0.1%
1199948148.14815 1
 
< 0.1%
69683935845.2139 1
 
< 0.1%
545982375701.128 1
 
< 0.1%
1546892142709.84 1
 
< 0.1%
Other values (1975) 1975
96.4%

Most occurring characters

ValueCountFrequency (%)
1 3497
11.4%
0 3106
10.1%
2 3015
9.8%
4 2868
9.3%
3 2867
9.3%
5 2755
8.9%
7 2730
8.9%
6 2693
8.7%
9 2667
8.7%
8 2663
8.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 28861
93.7%
Other Punctuation 1944
 
6.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 3497
12.1%
0 3106
10.8%
2 3015
10.4%
4 2868
9.9%
3 2867
9.9%
5 2755
9.5%
7 2730
9.5%
6 2693
9.3%
9 2667
9.2%
8 2663
9.2%
Other Punctuation
ValueCountFrequency (%)
. 1944
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 30805
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 3497
11.4%
0 3106
10.1%
2 3015
9.8%
4 2868
9.3%
3 2867
9.3%
5 2755
8.9%
7 2730
8.9%
6 2693
8.7%
9 2667
8.7%
8 2663
8.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 30805
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 3497
11.4%
0 3106
10.1%
2 3015
9.8%
4 2868
9.3%
3 2867
9.3%
5 2755
8.9%
7 2730
8.9%
6 2693
8.7%
9 2667
8.7%
8 2663
8.6%

population
Real number (ℝ)

Distinct2048
Distinct (%)100.0%
Missing138
Missing (%)6.3%
Infinite0
Infinite (%)0.0%
Mean36612266
Minimum10444
Maximum1.41236 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.2 KiB
2023-04-10T22:16:57.278026image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum10444
5-th percentile56176.25
Q11262415
median7145394
Q325663486
95-th percentile1.247103 × 108
Maximum1.41236 × 109
Range1.4123496 × 109
Interquartile range (IQR)24401072

Descriptive statistics

Standard deviation1.3951161 × 108
Coefficient of variation (CV)3.8105156
Kurtosis78.525556
Mean36612266
Median Absolute Deviation (MAD)6829983
Skewness8.5879579
Sum7.4981922 × 1010
Variance1.946349 × 1016
MonotonicityNot monotonic
2023-04-10T22:16:57.419828image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2873457 1
 
< 0.1%
37974750 1
 
< 0.1%
108568836 1
 
< 0.1%
32203944 1
 
< 0.1%
6443328 1
 
< 0.1%
9329227 1
 
< 0.1%
4165255 1
 
< 0.1%
219731479 1
 
< 0.1%
17864 1
 
< 0.1%
45989 1
 
< 0.1%
Other values (2038) 2038
93.2%
(Missing) 138
 
6.3%
ValueCountFrequency (%)
10444 1
< 0.1%
10694 1
< 0.1%
10828 1
< 0.1%
10852 1
< 0.1%
10854 1
< 0.1%
10865 1
< 0.1%
10877 1
< 0.1%
10899 1
< 0.1%
10918 1
< 0.1%
10940 1
< 0.1%
ValueCountFrequency (%)
1412360000 1
< 0.1%
1411100000 1
< 0.1%
1407745000 1
< 0.1%
1407563842 1
< 0.1%
1402760000 1
< 0.1%
1396387127 1
< 0.1%
1396215000 1
< 0.1%
1387790000 1
< 0.1%
1383112050 1
< 0.1%
1379860000 1
< 0.1%

j
Categorical

HIGH CARDINALITY  MISSING  UNIFORM 

Distinct192
Distinct (%)10.0%
Missing268
Missing (%)12.3%
Memory size137.8 KiB
AFG
 
10
NZL
 
10
NIC
 
10
NER
 
10
NGA
 
10
Other values (187)
1868 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters5754
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAFG
2nd rowALB
3rd rowDZA
4th rowAND
5th rowAGO

Common Values

ValueCountFrequency (%)
AFG 10
 
0.5%
NZL 10
 
0.5%
NIC 10
 
0.5%
NER 10
 
0.5%
NGA 10
 
0.5%
NOR 10
 
0.5%
FSM 10
 
0.5%
MHL 10
 
0.5%
PLW 10
 
0.5%
PAK 10
 
0.5%
Other values (182) 1818
83.2%
(Missing) 268
 
12.3%

Length

2023-04-10T22:16:57.549581image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
afg 10
 
0.5%
arm 10
 
0.5%
and 10
 
0.5%
ago 10
 
0.5%
atg 10
 
0.5%
aze 10
 
0.5%
arg 10
 
0.5%
aus 10
 
0.5%
aut 10
 
0.5%
bhs 10
 
0.5%
Other values (182) 1818
94.8%

Most occurring characters

ValueCountFrequency (%)
R 460
 
8.0%
A 440
 
7.6%
N 400
 
7.0%
M 389
 
6.8%
B 329
 
5.7%
G 320
 
5.6%
L 300
 
5.2%
T 299
 
5.2%
S 290
 
5.0%
C 249
 
4.3%
Other values (16) 2278
39.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 5754
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
R 460
 
8.0%
A 440
 
7.6%
N 400
 
7.0%
M 389
 
6.8%
B 329
 
5.7%
G 320
 
5.6%
L 300
 
5.2%
T 299
 
5.2%
S 290
 
5.0%
C 249
 
4.3%
Other values (16) 2278
39.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 5754
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
R 460
 
8.0%
A 440
 
7.6%
N 400
 
7.0%
M 389
 
6.8%
B 329
 
5.7%
G 320
 
5.6%
L 300
 
5.2%
T 299
 
5.2%
S 290
 
5.0%
C 249
 
4.3%
Other values (16) 2278
39.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5754
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
R 460
 
8.0%
A 440
 
7.6%
N 400
 
7.0%
M 389
 
6.8%
B 329
 
5.7%
G 320
 
5.6%
L 300
 
5.2%
T 299
 
5.2%
S 290
 
5.0%
C 249
 
4.3%
Other values (16) 2278
39.6%

dist
Real number (ℝ)

Distinct189
Distinct (%)10.0%
Missing298
Missing (%)13.6%
Infinite0
Infinite (%)0.0%
Mean9047.2762
Minimum809.5382
Maximum19297.47
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.2 KiB
2023-04-10T22:16:57.671088image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum809.5382
5-th percentile2850.319
Q16523.571
median8390.566
Q311903.59
95-th percentile14866.92
Maximum19297.47
Range18487.932
Interquartile range (IQR)5380.019

Descriptive statistics

Standard deviation3877.9301
Coefficient of variation (CV)0.42862956
Kurtosis-0.29941613
Mean9047.2762
Median Absolute Deviation (MAD)2650.464
Skewness0.2411037
Sum17081258
Variance15038342
MonotonicityNot monotonic
2023-04-10T22:16:57.815344image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14352.56 10
 
0.5%
11041.03 10
 
0.5%
13785.57 10
 
0.5%
11024.17 10
 
0.5%
11466.06 10
 
0.5%
7031.006 10
 
0.5%
5548.78 10
 
0.5%
6537.865 10
 
0.5%
4048.299 10
 
0.5%
3882.877 10
 
0.5%
Other values (179) 1788
81.8%
(Missing) 298
 
13.6%
ValueCountFrequency (%)
809.5382 10
0.5%
955.6511 10
0.5%
1172.047 10
0.5%
1976.249 10
0.5%
1982.745 10
0.5%
2098.111 10
0.5%
2330.799 10
0.5%
2778.652 10
0.5%
2812.561 10
0.5%
2850.319 10
0.5%
ValueCountFrequency (%)
19297.47 10
0.5%
19175.59 10
0.5%
19079.88 10
0.5%
18311.35 10
0.5%
17614.3 10
0.5%
17389.85 10
0.5%
16666.29 10
0.5%
15364.41 10
0.5%
14937.48 10
0.5%
14866.92 10
0.5%

sum_pos_tweets
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct685
Distinct (%)67.3%
Missing1168
Missing (%)53.4%
Infinite0
Infinite (%)0.0%
Mean13170.081
Minimum0
Maximum708560
Zeros71
Zeros (%)3.2%
Negative0
Negative (%)0.0%
Memory size34.2 KiB
2023-04-10T22:16:57.967767image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q117
median488
Q34194.5
95-th percentile55283.15
Maximum708560
Range708560
Interquartile range (IQR)4177.5

Descriptive statistics

Standard deviation55057.135
Coefficient of variation (CV)4.1804707
Kurtosis74.669214
Mean13170.081
Median Absolute Deviation (MAD)487
Skewness7.9359981
Sum13407142
Variance3.0312882 × 109
MonotonicityNot monotonic
2023-04-10T22:16:58.103966image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 71
 
3.2%
1 54
 
2.5%
2 20
 
0.9%
3 17
 
0.8%
4 14
 
0.6%
5 13
 
0.6%
6 11
 
0.5%
8 8
 
0.4%
7 8
 
0.4%
10 7
 
0.3%
Other values (675) 795
36.4%
(Missing) 1168
53.4%
ValueCountFrequency (%)
0 71
3.2%
1 54
2.5%
2 20
 
0.9%
3 17
 
0.8%
4 14
 
0.6%
5 13
 
0.6%
6 11
 
0.5%
7 8
 
0.4%
8 8
 
0.4%
9 3
 
0.1%
ValueCountFrequency (%)
708560 1
< 0.1%
646051 1
< 0.1%
570562 1
< 0.1%
510945 1
< 0.1%
510064 1
< 0.1%
445639 1
< 0.1%
431775 1
< 0.1%
332144 1
< 0.1%
283451 1
< 0.1%
281640 1
< 0.1%

count_tweets
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct752
Distinct (%)73.9%
Missing1168
Missing (%)53.4%
Infinite0
Infinite (%)0.0%
Mean30153.134
Minimum1
Maximum1557851
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.2 KiB
2023-04-10T22:16:58.253014image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q146
median1238.5
Q39314.25
95-th percentile124532.8
Maximum1557851
Range1557850
Interquartile range (IQR)9268.25

Descriptive statistics

Standard deviation124806.14
Coefficient of variation (CV)4.139077
Kurtosis72.637998
Mean30153.134
Median Absolute Deviation (MAD)1235.5
Skewness7.8440606
Sum30695890
Variance1.5576573 × 1010
MonotonicityNot monotonic
2023-04-10T22:16:58.395343image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 47
 
2.2%
2 30
 
1.4%
3 20
 
0.9%
4 14
 
0.6%
5 12
 
0.5%
6 8
 
0.4%
14 8
 
0.4%
12 7
 
0.3%
8 7
 
0.3%
17 6
 
0.3%
Other values (742) 859
39.3%
(Missing) 1168
53.4%
ValueCountFrequency (%)
1 47
2.2%
2 30
1.4%
3 20
0.9%
4 14
 
0.6%
5 12
 
0.5%
6 8
 
0.4%
7 5
 
0.2%
8 7
 
0.3%
9 6
 
0.3%
10 5
 
0.2%
ValueCountFrequency (%)
1557851 1
< 0.1%
1428281 1
< 0.1%
1360903 1
< 0.1%
1209281 1
< 0.1%
1107731 1
< 0.1%
1004543 1
< 0.1%
969659 1
< 0.1%
796335 1
< 0.1%
628125 1
< 0.1%
626655 1
< 0.1%

sum_political_tweets
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct752
Distinct (%)73.9%
Missing1168
Missing (%)53.4%
Infinite0
Infinite (%)0.0%
Mean30153.134
Minimum1
Maximum1557851
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.2 KiB
2023-04-10T22:16:58.547876image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q146
median1238.5
Q39314.25
95-th percentile124532.8
Maximum1557851
Range1557850
Interquartile range (IQR)9268.25

Descriptive statistics

Standard deviation124806.14
Coefficient of variation (CV)4.139077
Kurtosis72.637998
Mean30153.134
Median Absolute Deviation (MAD)1235.5
Skewness7.8440606
Sum30695890
Variance1.5576573 × 1010
MonotonicityNot monotonic
2023-04-10T22:16:58.687867image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 47
 
2.2%
2 30
 
1.4%
3 20
 
0.9%
4 14
 
0.6%
5 12
 
0.5%
6 8
 
0.4%
14 8
 
0.4%
12 7
 
0.3%
8 7
 
0.3%
17 6
 
0.3%
Other values (742) 859
39.3%
(Missing) 1168
53.4%
ValueCountFrequency (%)
1 47
2.2%
2 30
1.4%
3 20
0.9%
4 14
 
0.6%
5 12
 
0.5%
6 8
 
0.4%
7 5
 
0.2%
8 7
 
0.3%
9 6
 
0.3%
10 5
 
0.2%
ValueCountFrequency (%)
1557851 1
< 0.1%
1428281 1
< 0.1%
1360903 1
< 0.1%
1209281 1
< 0.1%
1107731 1
< 0.1%
1004543 1
< 0.1%
969659 1
< 0.1%
796335 1
< 0.1%
628125 1
< 0.1%
626655 1
< 0.1%

sum_likes
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct726
Distinct (%)71.3%
Missing1168
Missing (%)53.4%
Infinite0
Infinite (%)0.0%
Mean110792.33
Minimum0
Maximum6919036
Zeros109
Zeros (%)5.0%
Negative0
Negative (%)0.0%
Memory size34.2 KiB
2023-04-10T22:16:58.839300image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q117
median2131
Q326538
95-th percentile488111.3
Maximum6919036
Range6919036
Interquartile range (IQR)26521

Descriptive statistics

Standard deviation498488.8
Coefficient of variation (CV)4.4993079
Kurtosis84.355394
Mean110792.33
Median Absolute Deviation (MAD)2131
Skewness8.3926264
Sum1.1278659 × 108
Variance2.4849108 × 1011
MonotonicityNot monotonic
2023-04-10T22:16:58.978707image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 109
 
5.0%
1 44
 
2.0%
3 20
 
0.9%
2 19
 
0.9%
5 8
 
0.4%
11 8
 
0.4%
8 6
 
0.3%
4 6
 
0.3%
7 6
 
0.3%
23 6
 
0.3%
Other values (716) 786
36.0%
(Missing) 1168
53.4%
ValueCountFrequency (%)
0 109
5.0%
1 44
2.0%
2 19
 
0.9%
3 20
 
0.9%
4 6
 
0.3%
5 8
 
0.4%
6 3
 
0.1%
7 6
 
0.3%
8 6
 
0.3%
9 4
 
0.2%
ValueCountFrequency (%)
6919036 1
< 0.1%
6234509 1
< 0.1%
4990612 1
< 0.1%
4367973 1
< 0.1%
4181543 1
< 0.1%
3828365 1
< 0.1%
3652751 1
< 0.1%
3512024 1
< 0.1%
2857956 1
< 0.1%
2665921 1
< 0.1%

sum_retweeets
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct728
Distinct (%)71.5%
Missing1168
Missing (%)53.4%
Infinite0
Infinite (%)0.0%
Mean43813.677
Minimum0
Maximum2242006
Zeros109
Zeros (%)5.0%
Negative0
Negative (%)0.0%
Memory size34.2 KiB
2023-04-10T22:16:59.128208image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q116
median1193.5
Q312716.5
95-th percentile194828.45
Maximum2242006
Range2242006
Interquartile range (IQR)12700.5

Descriptive statistics

Standard deviation185469.94
Coefficient of variation (CV)4.2331518
Kurtosis68.946242
Mean43813.677
Median Absolute Deviation (MAD)1193.5
Skewness7.749181
Sum44602323
Variance3.43991 × 1010
MonotonicityNot monotonic
2023-04-10T22:16:59.265440image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 109
 
5.0%
1 41
 
1.9%
3 17
 
0.8%
4 13
 
0.6%
2 13
 
0.6%
6 10
 
0.5%
10 8
 
0.4%
7 7
 
0.3%
8 6
 
0.3%
14 6
 
0.3%
Other values (718) 788
36.0%
(Missing) 1168
53.4%
ValueCountFrequency (%)
0 109
5.0%
1 41
 
1.9%
2 13
 
0.6%
3 17
 
0.8%
4 13
 
0.6%
5 6
 
0.3%
6 10
 
0.5%
7 7
 
0.3%
8 6
 
0.3%
9 4
 
0.2%
ValueCountFrequency (%)
2242006 1
< 0.1%
1911858 1
< 0.1%
1860205 1
< 0.1%
1820632 1
< 0.1%
1786003 1
< 0.1%
1692160 1
< 0.1%
1574074 1
< 0.1%
1391875 1
< 0.1%
1100089 1
< 0.1%
991547 1
< 0.1%

Interactions

2023-04-10T22:16:48.137998image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T22:14:04.510192image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T22:14:15.889893image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T22:14:29.807339image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T22:14:41.293520image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T22:14:52.797016image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T22:15:04.156565image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T22:16:11.181387image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T22:16:21.699525image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T22:16:28.039371image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T22:16:34.410245image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T22:16:40.800117image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T22:16:48.263643image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T22:14:04.620988image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T22:14:15.992990image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T22:14:29.915719image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T22:14:41.403412image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T22:14:52.894131image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T22:15:10.001144image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T22:16:11.300263image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T22:16:21.831746image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T22:16:28.174653image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T22:16:34.545217image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T22:16:40.929127image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T22:16:48.388105image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T22:14:04.725493image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T22:14:16.094775image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T22:14:30.025654image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T22:14:41.512748image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T22:14:52.991690image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T22:15:16.337147image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T22:16:11.415174image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T22:16:21.964806image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T22:16:28.310889image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T22:16:34.682897image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T22:16:41.060941image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T22:16:48.494323image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T22:14:04.836229image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T22:14:16.205000image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T22:14:30.137208image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T22:14:41.626838image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T22:14:53.089191image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T22:15:22.183282image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T22:16:11.531042image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T22:16:22.081129image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T22:16:28.428245image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T22:16:34.805154image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T22:16:41.174606image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T22:16:48.601890image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T22:14:04.948937image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T22:14:16.318309image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T22:14:30.252062image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T22:14:41.740104image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T22:14:53.189113image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T22:15:28.028931image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T22:16:11.649037image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T22:16:22.197647image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T22:16:28.547900image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T22:16:34.925586image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T22:16:41.289901image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T22:16:48.684357image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T22:14:05.034770image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T22:14:16.403511image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T22:14:30.337847image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T22:14:41.826462image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T22:14:53.266419image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T22:15:34.401235image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T22:16:11.742942image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T22:16:22.287083image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T22:16:28.640637image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T22:16:35.020214image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T22:16:41.377615image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T22:16:53.657432image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T22:14:15.117683image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T22:14:29.034491image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T22:14:40.566595image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T22:14:52.071562image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T22:15:03.136119image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T22:15:49.957461image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T22:16:20.894359image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T22:16:27.264895image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T22:16:33.633568image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T22:16:40.014399image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T22:16:47.375357image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T22:16:53.783765image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T22:14:15.242422image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T22:14:29.158567image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T22:14:40.689175image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T22:14:52.195772image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T22:15:03.253774image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T22:15:55.344363image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T22:16:21.026647image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T22:16:27.400809image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T22:16:33.768942image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T22:16:40.152812image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T22:16:47.510694image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T22:16:53.903908image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T22:14:15.372116image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T22:14:29.287138image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T22:14:40.809827image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T22:14:52.315293image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T22:15:03.365187image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T22:15:58.334962image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T22:16:21.162807image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T22:16:27.526567image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T22:16:33.898911image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T22:16:40.284299image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T22:16:47.636581image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T22:16:54.027895image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T22:14:15.508204image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T22:14:29.424319image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T22:14:40.937665image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T22:14:52.440677image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T22:15:03.481363image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T22:16:01.330832image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T22:16:21.301398image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T22:16:27.660695image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T22:16:34.031421image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T22:16:40.418095image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T22:16:47.768851image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T22:16:54.154700image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T22:14:15.642896image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T22:14:29.560393image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T22:14:41.066029image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T22:14:52.568120image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T22:15:03.600219image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T22:16:04.349064image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T22:16:21.443263image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T22:16:27.795308image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T22:16:34.163606image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T22:16:40.552403image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T22:16:47.900495image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T22:16:54.272823image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T22:14:15.773067image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T22:14:29.690673image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T22:14:41.185537image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T22:14:52.689069image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T22:15:03.710736image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T22:16:07.355984image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T22:16:21.577060image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T22:16:27.922588image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T22:16:34.292926image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T22:16:40.681573image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-10T22:16:48.024866image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Correlations

2023-04-10T22:16:59.381421image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
RefYearPeriodCifvaluePrimaryValueyearpopulationdistsum_pos_tweetscount_tweetssum_political_tweetssum_likessum_retweeets
RefYear1.0001.0000.0270.0271.0000.0020.0020.7180.7040.7040.7970.727
Period1.0001.0000.0270.0271.0000.0020.0020.7180.7040.7040.7970.727
Cifvalue0.0270.0271.0001.0000.0360.133-0.2810.3150.3300.3300.2680.316
PrimaryValue0.0270.0271.0001.0000.0360.133-0.2810.3150.3300.3300.2680.316
year1.0001.0000.0360.0361.0000.0020.0020.7180.7040.7040.7970.727
population0.0020.0020.1330.1330.0021.000-0.128-0.057-0.058-0.058-0.058-0.058
dist0.0020.002-0.281-0.2810.002-0.1281.000-0.093-0.095-0.095-0.075-0.098
sum_pos_tweets0.7180.7180.3150.3150.718-0.057-0.0931.0000.9970.9970.9790.983
count_tweets0.7040.7040.3300.3300.704-0.058-0.0950.9971.0001.0000.9760.984
sum_political_tweets0.7040.7040.3300.3300.704-0.058-0.0950.9971.0001.0000.9760.984
sum_likes0.7970.7970.2680.2680.797-0.058-0.0750.9790.9760.9761.0000.984
sum_retweeets0.7270.7270.3160.3160.727-0.058-0.0980.9830.9840.9840.9841.000

Missing values

2023-04-10T22:16:54.458683image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-04-10T22:16:54.773946image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-04-10T22:16:55.022473image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

RefYearPeriodReporterISOFlowDescPartnerISOCifvaluePrimaryValueKeyCountry Codeyeargdppopulationjdistsum_pos_tweetscount_tweetssum_political_tweetssum_likessum_retweeets
020122012CHNImportW001.818199e+121818199227571W00_2012NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
120122012CHNImportAFG5.186565e+065186565AFG_2012AFG201220203572959.502330466479AFG4180.438NaNNaNNaNNaNNaN
220122012CHNImportALB1.427209e+08142720886ALB_2012ALB201212319830437.34672900401ALB7686.079NaNNaNNaNNaNNaN
320122012CHNImportDZA2.311906e+092311905609DZA_2012DZA2012209058991952.12537260563DZA9117.676NaNNaNNaNNaNNaN
420122012CHNImportAND3.240020e+05324002AND_2012AND20123188808942.5671371013AND8764.593NaNNaNNaNNaNNaN
520122012CHNImportAGO3.356190e+1033561896917AGO_2012AGO2012124998210652.24325188292AGO11769.510NaNNaNNaNNaNNaN
620122012CHNImportATG7.135100e+0471351ATG_2012ATG20121199948148.1481587674ATG13681.690NaNNaNNaNNaNNaN
720122012CHNImportAZE2.141617e+08214161731AZE_2012AZE201269683935845.21399295784AZE5520.214NaNNaNNaNNaNNaN
820122012CHNImportARG6.560806e+096560805532ARG_2012ARG2012545982375701.12841733271ARG19297.470NaNNaNNaNNaNNaN
920122012CHNImportAUS8.456821e+1084568208584AUS_2012AUS20121546892142709.8422733465AUS8956.436NaNNaNNaNNaNNaN
RefYearPeriodReporterISOFlowDescPartnerISOCifvaluePrimaryValueKeyCountry Codeyeargdppopulationjdistsum_pos_tweetscount_tweetssum_political_tweetssum_likessum_retweeets
217620212021CHNImportUSA1.809719e+11180971932243USA_2021USA202123315080560000331893745USA10993.680NaNNaNNaNNaNNaN
217720212021CHNImportBFA1.884526e+08188452598BFA_2021BFA202119737615114.366122100683BFA11404.3708612.019080.019080.0135312.045960.0
217820212021CHNImportURY3.623471e+093623470751URY_2021URY202159319484710.65273426260URY19175.5901754.04007.04007.020283.05608.0
217920212021CHNImportUZB1.540988e+091540987879UZB_2021UZB202169238903106.173834915100UZB3943.621NaNNaNNaNNaNNaN
218020212021CHNImportVEN9.977931e+08997793138VEN_2021VEN2021..28199867VEN14402.5001107.02434.02434.017253.04293.0
218120212021CHNImportWLF1.475400e+0414754WLF_2021NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
218220212021CHNImportWSM6.432650e+05643265WSM_2021WSM2021843842416.462442218764WSM8268.3197.012.012.029.018.0
218320212021CHNImportYEM4.708126e+08470812557YEM_2021YEM2021..32981641YEM7417.418NaNNaNNaNNaNNaN
218420212021CHNImportZMB4.385251e+094385251435ZMB_2021ZMB202122147634727.358419473125ZMB10960.7902397.05458.05458.018965.05874.0
218520212021CHNImport_X2.060227e+092060227074_X _2021NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN